Predicting Daily Heating Energy Consumption of Rural Residences in Northern China Using Support Vector Machine

Transcription

1 Predicting Daiy Heating Energy Consumption of Rura Residences in Norern China Using Support Vector Machine P.L. Yuan, L. Duanmu and Z.S.Wang Schoo of Civi Engineering Daian University of Technoogy, Daian, Liaoning , China SUMMARY It is not feasibe to appy e engineering prediction meod to predict e daiy heating energy consumption of Chinese rura residences due to e imited and uncertain parameters, such as air infitration. This paper presents a data driven meod-support vector machine to predict e daiy heating energy consumption of Chinese rura residences. The data of heating energy consumption is coected by testing raer an questionnaire survey. A new input parameter, e cumuative temperature difference between indoor and outdoor air Tc is proposed instead of average indoor air temperature because of e different temperature variation rues between e rura and urban residences. The resuts demonstrate at e predicted daiy heating energy consumption of rura residences has a good agreement wi e measured vaue. The mean reative error is ower an 10%. This study furer forecasts e daiy heating energy consumption from 1st January to 30 March. INTRODUCTION In China, e energy consumption in norern rura residences accounts for 42% of e nationa buiding energy consumption. The average outdoor air temperature in January is beow 0. Thus, space heating is essentia for sustaining e indoor erma comfort in norern China. The heating energy consumption has reached up to 105 miion tce in norern rura residences (BECRC, Tsinghua University 2016). The widey used fues for heating are buk coa, wood, cornstaks and so on. These fues are directy burned in e decentraized heating equipments, incuding Chinese Kang (Zhuang Z et a. 2009), hot-wa Kang (Duanmu L et a. 2017), and sma boier wi radiators and so on. The exhaust gas generated by burning e soid fues is removed from chimney directy wiout any treatment measures. Thus, it is imperative to decrease e energy consumption of Chinese rura residences. However, e foundation of energy conservation is e actua energy consumption. As described above, e heating energy consumption in Chinese rura residences is difficut to measure using meter devices. Generay, ere are muti-sources and different channes to obtain e heating energy consumption of Chinese rura residences (Yao C et a 2012). The primary source of rura energy consumption for China is e Nationa Statistics Bureau, which posses e energy consumption of energy sources. However, e end-use energy consumption is not incuded. The common meod for achieving e heating energy consumption of Chinese rura residences is e random stratified samping wi questionnaire survey. Tsinghua University aunched severa nationwide energy consumption investigations in Chinese rura areas in 2006, 2007 and 2015 (BECRC, Tsinghua University 2012, 2016). Zhou Z (2009) anayzed e househod energy consumption in terms of energy sources and energy end-use in viages of Huantai County from 1989 to The data concerning e energy consumption were a coected using questionnaires. Wang Y (2016) investigated e heating energy consumption rough questionnaires in Chinese rura residences. The questionnaire survey is a simpe and straight approach to obtain e heating energy consumption of Chinese rura residences. Rura househods ony need answer e questions in e questionnaire, and en researchers anayze e data rough statistica meod. Noneeess, e investigation resuts have a strong reationship wi e number of sampes and e quaity of e questionnaire. At present, e investigation process is not transparency and e quaity of questionnaire survey cannot be assessed. The heating energy consumption of Chinese rura residences can be achieved by prediction mode as we. Deveoping reiabe and accurate mode for predicting buiding energy consumption is a chaenge project (Wiiam K et a. 2016). Various modes for predicting heating energy consumption are based on e dynamic heat transfer (Fischer D et a 2016), and data driven meod (Paude S et a. 2014).Meanwhie, severa buiding energy consumption simuation software (DOE-2, equest, EnergyPus, Dest) have been deveoped and appied. The forecasting meod for e heating energy consumption of Chinese rura residences concentrates on dynamic heat transfer and simuation software. Some researchers deveoped e dynamic heat transfer mode to predict e indoor air temperature in Chinese rura residences ( Yang M et a. 2013; Cao G et a. 2011). Thus, e heating energy consumption can be obtained rough setting e demanded indoor air temperature in e dynamic heat transfer mode. Dest is widey used to simuate e heating energy consumption of Chinese rura residences. The prediction modes based on e dynamic heat transfer require a arge number of physica parameters, such as e air infitration, buiding size, heat transfer coefficient of buiding enveope, and so on. Moreover, some physica parameters are uncertain for Chinese rura residences. As we a know, e prediction mode of energy consumption shoud have extensive and convenient feature. Aough e modes based on e dynamic heat transfer can guide e energy conservation measure effectivey, ey are compicated for common users to forecast e heating energy consumption of rura residences. Fortunatey, prediction modes based on e data driven approaches have been deveoped graduay wi e imited parameters. These modes are suitabe for e common users. Among ese data driven approaches, support vector machine (SVM) possesses higher superiority an artificia neura network in terms of accuracy and generaization abiity (Izadyar N et a. 2015; Li Q et a. 2009). Aso, SVM can avaiaby tacke e sma sampe probem. Kavakiogu K page 345

2 (2011) estabished e Turkey s eectricity consumption prediction mode by empoying e support vector regression. The number of sampes is 32. It is very difficut to coect a arge number of heating energy consumption data by experiments in Chinese rura areas. Synesizing e two merits described above, SVM is appropriate for predicting e heating energy consumption of Chinese rura residences. To e best of our knowedge, few studies focus on appying e data driven meod to estabish e heating energy consumption prediction mode of rura residences based on e measured data. SVM modes of e pubished iteratures focused on e energy consumption of urban residences. None of previous studies has captured in predicting e heating energy consumption of Chinese rura residences using SVM. Comparing wi urban residences, e indoor air temperature of rura residences is unabe to hod steady. The reason is at rura househods generay go rough e process of igniting e coa, fiing e coa, damping down e fire and stopping burning every day. Thus, e input parameters such as average outdoor weaer data and average indoor air temperature empoyed in e previous studies is not appicabe for rura residences. The target of is paper is to estabish e SVM prediction mode of heating energy consumption at can be appied for Chinese rura residences. In is paper, e data of heating energy consumption of rura residences is coected by fied test instead of questionnaire survey. Fied test has higher superior an questionnaire survey in terms of e data quaity. Two rura residences are seected to vaidate e feasibiity of e SVM prediction mode of heating energy consumption in Chinese rura residences. METHODS SVM mode SVM guarantees e maximum generaization abiity of e mode based on e structura risk minimization, which can overcome e dimension disaster and over fitting probem (Vapnik V 1995). Assuming at e input parameters compose e vector Xi, Yi is e corresponding output parameters of Xi. The sampe set can be defined to be { Xi, Yi }in when e number of sampes is N. The maematica structure of SVM is demonstrated by e foowing equation: Y f ( X ) W ( X ) b 1 W 2 2 C (1) 1 L (Yi, f ( X i )) (2) where φ(x) is e noninear high dimensiona feature space formed by e input space X, The coefficient W and b shoud be determined rough e structura risk minimization meod, C represents e penaty parameters, is e number of training sampes, which are seected from e N sampes, and Lε (Yi, f(x)) is defined to be e ε-intensive oss function. The computing meod of Lε (Yi, f(x)) can be found in e iterature (Dong B et a. 2005). The structura risk function shoud be minimized to obtain e coefficient W and b. In order to sove e minimization probem more easiy, e dua eory is adopted and minimization probem is transformed to be e quadratic programming probem (Li Q et a. 2009). Minimize: 1 ( i i* )( j j * ) ( X i ) ( X j ) Yi ( i i* ) ( i i* ) 2 j 1 (3) Subject to: ( i* ) 0 (4) 0 i, i * C (5) i Thus, function (1) becomes e expicit form: f ( X ) ( i i* ) ( X i ) ( X ) b ( i i* ) K ( X i, X ) b (6) where αi and αi* are e agrange mutipier vector and shoud be greater an zero. K (Xi, X) is e kerne function and equa to φ (Xi) φ (X). The conventiona kerne function K (Xi, X) contains e inear function, poynomia function, Gaussian RBF function, exponentia base function and sigmoid function. Among ese kerne functions, Gaussian RBF function can map e input space into e high dimensiona feature space effectivey, and makes e compex noninear reationship between e input and output parameters present preferaby. In is paper, Gaussian RBF function is seected to be e kerne function. The function form can be found in e iterature (Izadyar N et a. 2015). In e SVM mode, e parameter C, ε and σ2 shoud be optimized so at e mode can accuratey predict e unknown data. This paper first empoys partice swam optimization (PSO) to optimize C and σ2. Then, C and σ2 are fixed to be e optima vaue, and set ε as various vaues in a range and train e SVM mode. The optima vaue of ε wi be determined based on e prediction performance indices and e number of support vectors. The performance evauation indicators of SVM mode adopted rough is paper are root mean square error RMSE, mean reative error MRE and e determination coefficient between predicted and measured vaue R2. The cacuation meod can be referenced in e iterature (Li Q et a. 2009; Izadyar N et a. 2015) Input parameters Many previous researchers adopted e average outdoor air temperature, goba soar radiation (GSR) and average indoor air temperature as e input parameters in urban residences (Izadyar N et a. 2015). As we a know, e indoor air temperature of urban residences is persistenty steady. The average indoor air temperature can refect e effect of heating energy consumption. Noneeess, e indoor air temperature of rura residences has a arger fuctuation because e heating is intermittent. The average indoor air temperature cannot present e function generated by e actua heating energy consumption. Thus, we need propose a new input parameter reated to indoor air temperature at can preferaby present e function of heating energy consumption in rura residences. Niu S (2010) demonstrated at e difference between e indoor and outdoor air temperature was primariy e resut of heating. The cumuative temperature difference between indoor and outdoor air is shown in e foowing forms: Tc Tindoor Toutdoor (7) page 346

3 n (8) Tindoor tindoor d 0 n Toutdoor t outdoor d (9) 0 where Tc represents e cumuative temperature difference between indoor air and outdoor air, s. Tindoor and Toutdoor are e cumuative indoor air and outdoor air temperature respectivey, s. tindoor and toutdoor are e indoor air and outdoor air temperature respectivey,. τ is time, s; n is e fina time, s. Ref (Niu S et a. 2010) aso presented at an increase in e indoor air temperature from Toutdoor to Tindoor required e consumption of a certain amount of fue. In is paper, e input parameters of SVM mode are Tc and GSR. Tabe 2. Measurement instruments Measurement parameters Fue mass Indoor and outdoor air temperature Soar radiation Measurement instruments Eectronic scae Measurement accuracy 5g Thermo Recorder ±0.5, ±5%RH Vantage Pr portabe weaer station ±5% During e test, we assumed at e externa door and windows were cosed a day. Moreover, we didn t consider e variation of e number of personne in e room. RESULTS Data coection Heating energy consumption Through anayzing e input parameters, e houry indoor and outdoor air temperature and soar radiation shoud be measured. Two rura residences are seected in a viage, ocated in Chifeng, Inner Mongoia. They are a traditiona rura residences shown in Figure 1. The daiy heating energy consumption is measured by rura househods every day. To our best knowedge, is is first time to measure e daiy heating energy consumption in rura areas. The measured data is more objective an questionnaire survey. In e two rura residences, e heating equipment is couped heating system of Chinese Kang and sma boier wi radiators. The fues for heating contain coa and biomass. The biomass fues are burned in e stove and e gas heats e Kang surface. Coa is burned in e sma boier. Figure 2 showed at e coa consumption payed a dominant roe in heating e indoor air. The biomass consumption fuctuated around 5kgce. However, e biomass consumption beyond 10kgce severa days, which had arger difference an e oer days. The reason is at some visitors come to is rura residence and e rura househod has to prepare more food. Thus, e stove is used more frequenty an ordinary day. a) Rura residence A b) Rura residence B Figure 1. Two test rura residences The fied test began in November and asted 44 days. However, e number of sampes is different for e two residences. In rura residence A, e data from 21 November to December 25 are e training sampes, and e residua data are e testing sampes. In addition, e data from December 2 to December 12 are omitted because e data of soar radiation and indoor air temperature are missing during is period. In rura residence B, e data from November 23 to December 25 are e training sampes, and e residua data are e testing sampes. The data from December 2 to December 8 are omitted because e data of soar radiation is missing during is period. The buiding information and measurement instruments are isted in Tabe 1 and Tabe 2. Tabe 1. Buiding information Rura residence number Heating areas (m2) A B Heating equipment Chinese Kang and sma boier wi radiators Chinese Kang and sma boier wi radiators Number of heating room 2 3 Figure 2. Heating energy consumption of e two test rura residences Chinese Kang is a kind of heating equipment. If ere is ony Chinese Kang in e room, Chinese Kang pays a dominant roe in heating e indoor air. However, in e couped heating system of Chinese Kang and distributed sma boier wi radiators, wheer e biomass consumption burned in e stove infuences on e indoor air temperature remarkaby is unknown. This paper adopts e partia correation anaysis to anayze e infuence of coa consumption and biomass consumption on Tc. The partia correation anaysis is presented in Tabe 3. The resuts demonstrated at coa consumption impacted on Tc. significanty. The biomass consumption affected e indoor air temperature itte. The biomass is neary for cooking and e residua gas heats e Kang surface for rura househods page 347

4 sitting or seeping. The resuts indicated at e heating energy consumption was approximate to be e coa consumption in e couped heating system of Chinese Kang and sma boier wi radiators. Thus, e coa consumption was regarded as e output variabe in SVM modes. Tabe 3. Partia correation anaysis between coa consumption and Tc, and biomass consumption and Tc Variabes Paritia correation coefficient Significance eve Coa consumption Biomass consumption Parameters seection The accuracy of SVM mode is infuenced by e parameters (C, ε and σ2). Firsty, C and σ2 were optimized by PSO meod, and e vaue of optima C and σ2 are presented in Tabe 4. Tabe 4. Optima C and σ2 of e two rura residences Rura residence C σ2 A B estabished training modes shoud be appied to vaidate e testing data. Thus, e prediction resuts on testing data are more significant to evauate e mode performance. The performance evauation of SVM modes are presented in Tabe 5. Tabe 5 indicated at e overa mode performance of rura residence B was higher an rura residence A. Tabe 5. Performance evauation of SVM modes on e test data Rura residence RMSE MRE R2 A B To evauate e SVM mode performance furer, e measured vaue was compared wi e predicted vaue shown in Figure 4. The reative error between e predicted and measured vaue of severa sampes were arger an 15%. However, Figure 4 demonstrated at e predicted heating energy consumption had a good agreement wi e measured vaue. The mean reative errors were a ower an 10%. Thus, e estabished SVM mode can be considered to be reasonabe and vaid. Aso, e resuts indicated at SVM can be used to predict e heating energy consumption of rura residences. It does not need more prior information and use ski. Then, C and σ2 are fixed on e optimum vaue, and set ε as various vaues in a range and train e SVM mode. The resuts of various ε in e two rura residences are shown in Figure 3. a) Rura residence A a) Rura residence A b) Rura residence B Figure 3. Resuts of various ε in e two rura residences SVM mode vaidation b) Rura residence B Figure 4. Measured vaue and predicted vaue in e two rura residences Heating energy consumption prediction After e optima vaue of C, ε and σ2 confirmed, e train modes of e two rura residences were estabished. The The heating energy consumption of rura residences was measured by rura househods manuay. Furermore, is page 348

5 paper simpy measured e heating energy consumption of 44 days due to e imit of time and manpower. As described above, e estabished SVM mode can be appied to predict e heating energy consumption at is not measured by testing instruments. This study wi predict e heating energy consumption of e two rura househods from 1st January to 30 March. The weaer data derived from e specia meteoroogica data set of China buiding erma environment anaysis. The houry outdoor air temperature tout ( ) and soar radiation Isoar (W/m2) in Chifeng, Inner Mongoia, are shown in Figure 5. According to e input parameters, e demanded indoor air temperature shoud be gained before predicting e heating energy consumption. In is study, e demanded indoor air temperature was assumed to be a various vaue instead of constant vaue. The reason is at e heating equipment is operated intermittenty every day. The indoor air temperature of rura residences cannot sustain steady state. This paper takes e average houry indoor air temperature during e testing period as e demanded indoor air temperature. The resuts are shown in Figure 6. Figure 6 showed at ere are two peak vaues. The reason is at rura househods generay operate e sma boier in e morning and at sunset. residence B, e genera tendency of heating energy consumption presented differenty. The reason is possiby at e accuracy of e SVM mode is inadequate, which eads to e inaccurate resuts when e outdoor air temperature and soar radiation is reative high. Figure 7. Predicted heating energy consumption of two rura residences DISCUSSION This paper appied SVM to predict e heating energy consumption of Chinese rura residences based on e detaied test data. Due to e discrepancy of indoor air temperature between rura and urban residences, e cumuative temperature difference between indoor air and outdoor air Tc was proposed to be one of input parameters. The heating equipment of e two rura residences is e couped heating system of Chinese Kang and sma boier wi radiators. In genera, e heating energy consumption shoud be e sum of e fues consumption burned in e stove and sma boier. However, Tabe 3 showed at e biomass consumption affected e indoor air temperature itte. Thus, e heating energy consumption is assumed to be e coa consumption burned in e sma boier. Figure 5. Chifeng Outdoor air temperature and soar radiation in The SVM modes of e two rura residences were estabished and e mode performance was evauated. The mean reative error of e SVM modes was ower an 10%. R2 is around 0.5. The deviation between measured and predicted heating energy consumption is acceptabe. However, e prediction performance of e SVM modes shoud be improved according to e source of error. The possibe sources of error in e SVM modes are presented as foows. Figure 6. Demanded indoor air temperature of two rura residences The heating energy consumption of e two rura residences from 1st January to 31 March is predicted shown in Figure 7. The resuts showed at e heating energy consumption was fuctuated wi e outdoor cimatic conditions. The heating energy consumption of rura residence A presented a norma change, which is at, e genera tendency of e heating energy consumption decreased wi e outdoor air temperature and soar radiation increasing. However, in rura (1)The heating energy consumption in Chinese rura residences coud not be automaticay measured by recorders. Rura househods weighed e daiy fues for heating and en recorded e data in e og sheets. The weighting and recording process of e daiy fues reied on manua operation purey, which may resut in deviation and unreasonabe data. (2)The outside door is opened frequenty due to e ife stye of rura househods. Furermore, rura househods visit each oer, which resuts in e variation of e number of indoor occupants. Thus, e measured indoor air temperature may have deviation wi e actua indoor air temperature. page 349

6 Aough e SVM modes have a certain error, is paper vaidated e feasibiity of SVM to predict e heating energy consumption of Chinese rura residences. Moreover, is paper expored a new avenue to predict e heating energy consumption of Chinese rura residence. SVM mode can be appied not ony for buiding designer or researchers, but aso for common users. CONCLUSIONS This study carried out e heating energy consumption test of Chinese rura residences to provide better quaity data for deveoping SVM mode. SVM modes of e two rura residences were estabished. The input and output parameters for rura residences have been discussed. The cumuative temperature difference between indoor air and outdoor air Tc instead of average indoor air temperature was regarded as one of input parameters. Based on partia correation anaysis, e heating energy consumption was assumed to be e coa consumption, raer an e sum of coa and biomass consumption. Fischer D. et a A stochastic bottom-up mode for space heating and domestic hot water oad profies for German househods. Energy and Buidings, Vo. 124, pp Izadyar N. et a Appraisa of e support vector machine to forecast residentia heating demand for e District Heating System based on e mony overa natura gas consumption. Energy, Vo. 93, pp Kavakiogu K Modeing and prediction of Turkey s eectricity consumption using Support Vector Regression. Appied Energy, Vo. 88, pp Li Q. et a Predicting houry cooing oad in e buiding: A comparison of support vector machine and different artificia neura networks. Energy Conversion and Management, Vo. 50, pp Niu S. et a Energy demand for rura househod heating to suitabe eves in e Loess Hiy Region, Gansu Province, China. Energy, Vo. 35, pp The heating energy consumption predicted by SVM modes of e two rura residences has a good agreement wi e measured heating energy consumption. The mean reative error is ower an 10%, and R2 is around 0.5. This paper aso predicts e daiy heating energy consumption of e two rura residences from 1st January to 31 March. The prediction resuts are in consistent wi e reguar change. Noneeess, when e outdoor air temperature and soar radiation is reative high, e predicted resuts present abnormay. The prediction performance of SVM modes shoud be improved in e future. Paude S. et a Pseudo dynamic transitiona modeing of buiding heating energy demand using artificia neura network. Energy and Buidings, Vo. 70, pp ACKNOWLEDGEMENT Wiiams K. and Gomez J Predicting future mony residentia energy consumption using buiding characteristics and cimate data: A statistica earning approach. Energy and Buidings, Vo. 128, pp This research is supported by China nationa key research and deveopment program - Intergovernmenta cooperation in science and technoogy innovation (Project No. 2016YFE ). REFERENCES Buiding energy conservation research center, Tsinghua University China Buiding energy efficiency annua deveopment report in Beijing: China Buiding Industry Press. (In Chinese) Buiding energy conservation research center, Tsinghua University China Buiding energy efficiency annua deveopment report in Beijing: China Buiding Industry Press. (In Chinese) Cao G. et a Simuation of e heating performance of e Kang system in one Chinese detached house using biomass. Energy and Buidings, Vo. 43, pp Duanmu L. et a Heat Transfer Mode of Hot-wa Kang Based on e Non-uniform Kang Surface Temperature in Chinese Rura Residences. Buiding Simuation: An Internationa Journa, Vo. 10, Iss. 2, pp Vapnik V N The Nature of Statistica Learning Theory. New York: Springer -Verag, pages. Wang Y. et a Heating energy consumption questionnaire and statistica anaysis of rura buidings in China. Proceedings of e 8 Internationa Cod Cimate HVAC 2015 conference, Daian, China, Vo. 146, pp Wang P. et a Therma performance of a traditiona Chinese heated wa wi e in series fow pass: Experimenta and modeing. Energy and Buidings, Vo. 84, pp Yao C. et a Anaysis of rura residentia energy consumption and corresponding carbon emissions in China. Energy Poicy. Vo.41, pp Yang M. et a A new Chinese soar Kang and its dynamic heat transfer mode. Energy and Buidings, Vo. 63, pp Zhuang Z. eta Chinese kang as a domestic heating system in rura norern China A review. Energy and Buidings, Vo. 41, pp Zhou Z. et a Anaysis of changes in e structure of rura househod energy consumption in norern China: A case study. Renewabe and Sustainabe Energy Review. Vo.13, pp Dong B. et a Appying support vector machines to predict buiding energy consumption in tropica region. Energy and Buidings, Vo. 37, pp page 350